Capture The Flag Platform Review

Hi friends!

I will give some review for Capture The Flag training dojos, which I previously used. The platform made different methods to learn, as the competition website usually not forever.

1. Hackthebox 

Image result for hackthebox

This platform use unique membership appointment. Yeah, YOU SHOULD HACK IT TO GET THE MEMBERSHIP. I would rate the membership challenge as 4/10 in difficulty.

Hackthebox focused on penetration testing by providing some machine to be hacked. Beside that, they give you CTF-type challenges (not so many). The write up usually would be available after the machines retired.

I think it is a good idea to get the VIP access, since it grant you the access to the retired machine.  Remind me after I finish my thesis to get the VIP.

2. CTF365

Related image 

This is the most friendly platform – yet the most pricey. Still, you have 30 days free membership to try. I had taken the free trial, and would like to get the student membership for USD15 per month.

The platform has good educational approach for making it an online game – something that educational institution should try. I don’t know why using this platform increase my self confidence.

3. Adworld CTF

Image result for adworld ctf

The platform would contains many untranslated language – still many in Chinese for the events. Best thing it has slight learning curve, and you could start it from zero with google translate helps. This platform use the same methodology as CTF365, and available in exercise area and challenge area.

Wanna train your hacking skill and your Chinese language? I can relate. The best on it.

4. PicoCTF

Image result for picoctf

Best for newcomers. I will never forget this platform as where I am from. In picoCTF they train you the basics syntax to tools. The platform was designed for high school students, but it is not late for you to try. The competition held every year, but the webpage didn’t closed after the official competition finished, so you could made your way into it.

I would recommend everyone to learn from this one if you are a blank paper in CTF.



ps: I would update this post, the potential review would be:


facebook CTF

and some local run repository based CTF




OpenAI is a research organization that promotes friendly artificial intelligence. The phrase “friendly” come from the beneficial of AI to the humankind.

OpenAI was founded by Elon MuskSam AltmanIlya Sutskever, and Greg Brockman. Elon has concern of the dangers coming from AI. [1] Some university student also marshaling a demonstration in May Day regarding robots overtaking labors works.

Straightforward, we are jumping to the useful resources. You could get it here:


There is a tool named Gym, a system for learning. Gym is a tool for developing and improving reinforced learning. Reinforced learning is a branch of machine learning, which agents or software could took and action, and thus maximize it.

I will try Gym now, and later we will use Baseline, since I am newbie into Baseline.  Click the Gym. it will redirect you to

Lets try the introduction model!

To install Gym, open your terminal and type

pip install gym


Now we gonna try the sample source code shown in the main page

import gym
env = gym.make("CartPole-v1")
observation = env.reset()
for _ in range(1000):
  action = env.action_space.sample() # your agent here (this takes random actions)
  observation, reward, done, info = env.step(action)
  if done:
    observation = env.reset()

It was a game that use agent. It was said that:

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.

This environment corresponds to the version of the cart-pole problem described by Barto, Sutton, and Anderson [Barto83].

[Barto83] AG Barto, RS Sutton and CW Anderson, “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problem”, IEEE Transactions on Systems, Man, and Cybernetics, 1983.
      You could visit the source code here too:
    There is a menu named environment, where you could try the models.
        it was linked to:



Darknet & YOLO object detection

Finally it’s coming, since my laptop was under service before.

This time I’ll write about Darknet and YOLO Object Detection, and some tutorial on it. I know them from humanoid robot research, which needs computer vision and machine learning to detect object.


to install darknet:

Easily, darknet is the main program to run, and YOLO is the library used.

Yolo is a real time object detection system. It uses dataset to train what contained in an image. Not just image, you could capture the moment from webcam, like a humanoid robot did (Yeah I miss them so much).

In the website, it was said:


How YOLO Works?

It was said that:

Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.

We use a totally different approach. We apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.

Our model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image. It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. This makes it extremely fast, more than 1000x faster than R-CNN and 100x faster than Fast R-CNN. See our paper for more details on the full system. [1]

The technical step showed here:

This is my trained image!




Paralaks, Cara Mengukur Jarak Benda Langit

Mungkin kalian pernah memperkirakan tinggi atau lebar suatu objek, misalnya pohon atau gedung. Bukan dengan cara mengukurnya, namun membandingkannya dengan objek lain.

Oke, ikuti apa yang saya perintahkan. Pertama, hadapkan wajah ke tembok atau apapun yang backgroundnya polos. Kedua, letakkan jari kalian di antara mata, sekitar 10 cm dari hidung kalian. Lalu, tutup sebelah mata ecara bergantian. Dari situ kalian akan melihat jari kalian jatuh di titik yang berbeda di tembok itu. Begitulah konsepnya.

Kalian punya pertanyaan kenapa kita bisa mengetahui jarak Matahari dari Bumi waktu kecil? iya saya juga. Memangnya ada yang ngukur?

Mari kita berbicara tentag paralaks. Dalam astronomi, paralaks trigonometri dapat digunakan untuk memperkirakan jarak objek tertentu di langit. Misalnya bintang.

Caranya, kita mengetahui kalau bumi kita ini mengelilingi Matahari. nah kita ambil dua titik yang berseberangan, lalu kita lihat bintang itu dimana. Untuk menentukannya, kita bisa lihat bintang itu bergeser berapa jauh, menandakan dimana bintang itu jatuh di langit sebagai background.

Nah, dari dua posisi bumi terhadap matahari lalu ditarik garis lurus ke bintang, itu yang disebut sudut paralaks. dengan jarak bumi ke matahari diasumsikan 1 AU, kita bisa mencari jaraknya dengan formula trigonometri. Sebenarnya pengukuran yang ini pada praktiknya lebih advanced sih.

Sudut yang diukur saat bintang berpindah posisi jika dilihat sama dengan sudut jika bumi bergerak saat dilihat dari bintang. Karena perpindahan bumi yang ditempuh dalam 6 bulan sebesar dua kali jarak bumi matahari (bisa dilihat di gambar)

Gambar dibuat dengan Autodesk Inventor

Misalnya jarak bumi-matahari 1 AU, dan p dalam radian.

tan paralaks = 1 AU/jarak, atau jarak ke bintang = 1 AU/tan paralaks

karena sudut paralaks dalam radian amat kecil, maka dapat diasumsikan tan p = p


p = 1AU/jarak, atau jarak ke bintang = 1 AU/p

besar sudut paralaks dinyatakan dalam radian, namun dapat dikonversi ke detik busur.

Diketahui juga bahwa 1 detik paralaks atau parsec adalah 206256 AU, sebagai perbandingan jika sebuah bintang memiliki sudut paralaks 1 detik busur maka jaraknya adalah 206256 AU atau 1 parsec.

Referensi :

The Parallax Angle — How Astronomers Use Angular Measurement to Compute Distances in Space. diakses di

Astronomical Distances. diakses di

Tutorial Autodesk Inventor – Space Debris

Yang pertama ini temanya sampah luar angkasa, entah bagian apa ini. Tapi ini paling gampang dibuat (ya ga gampang-gampang amat sih).

Skill requirement paling tutorial dari inventornya.

Untuk pembuatan ini digunakan Autodesk Inventor 2014.

Oke langkah – langkahnya :

1. Buka inventor, pilih standart.ipt, atau expand tab template, pilih metric, terus pilih standart.ipt yang mm (ini preferensi saya buat pake mm sih).

2. akan terbuka workfield kosong, nah, klik create 2D sketch, terus pilih bidang apa aja (XY, XZ, atau YZ). nanti akan ada tampilan begini

3. Selanjutnya di menu sketch, pilih Line. buat bentuk seperti berikut

4. Untuk hasil yang lebih baik, pilih dimension. Buatlah dimensi contohnya seperti ini. Dimensi dapat disesuaikan namun harus berdasarkan geometri yang realistis. 

5. Setelah dimensi jadi, maka klik 3D model, maka akan muncul tampilan 3 dimensi. Lalu klik Revolve untuk memutar bentuk. klik pada sisi luar yang lurus (sisi 10 mm) seperti yang ditunjuk di gambar. Setelah itu klik centang hijau.

6. Maka akan muncul gambar seperti ini

7. Untuk hasil yang lebih baik dapat dipilih material. Pilih pada menu yang ditunjukkan kursor. Saya memilih Steel-carbon.

8. Beri thread. Dari menu 3D model klik thread, lalu tunjuk pada bagian bawah, maka akan otomatis membentuk. lalu setelah terlihat, klik apply atau ok.

9. Oke. Sekarang benda sudah jadi. Untuk melihat-lihat silakan gunakan panel di sebelah kanan atau putar-putar kotaknya.

10. [Advance] mari kita render. Klik environment, pilih inventor studio, pilih render image. Untuk menampilkan hasil sampah luar angkasa, pilih background galaxy atau starfield. Dan inilah hasilnya.

Yap, begitulah. Untuk bentuk yang lebih advance silakan dicoba ! mari kita buat versi alien !